Transactions on Emerging Telecommunications Technologies最新文献

{"title":"A Systematic Review of New Technologies for Cybersecurity Healthcare Applications: A Systematic and Comprehensive Study","authors":"Fatma Khallaf,&nbsp;Walid El-Shafai,&nbsp;El-Sayed M. El-Rabaie,&nbsp;Fathi E. Abd El-Samie","doi":"10.1002/ett.70183","DOIUrl":"https://doi.org/10.1002/ett.70183","url":null,"abstract":"<div>\u0000 \u0000 <p>The integration of the Internet of Things (IoT) into consumer devices has driven the evolution of the Industrial Internet of Things (IIoT), also known as Industry 4.0 (I4.0), extending connectivity to industrial settings where benefits such as increased efficiency, automation, and predictive maintenance are transforming processes. However, with these advancements comes a host of cybersecurity challenges unique to IIoT, including the longevity of industrial components and the expansive scale of interconnected networks, which differ from security needs in Consumer IoT (C-IoT). In parallel, the healthcare sector has seen similar technological integration through the Healthcare Internet of Things (H-IoT) and the progression to Healthcare 4.0 (HC4.0), emphasizing data-driven patient care and seamless digital health services. This paper presents a comprehensive and systematic review of emerging cybersecurity technologies in healthcare, focusing on IoT, IIoT, H-IoT, and HC4.0 applications. Our study examines recent advancements in cybersecurity protocols and identifies critical security challenges that arise from the increased reliance on these technologies. Specifically, we aim to highlight how these interconnected frameworks can enhance patient data protection, ensure resilience against cyber threats, and strengthen healthcare systems' operational integrity. Key areas of focus include data privacy, network vulnerabilities, and the risks of cyber-attacks in healthcare contexts, with an emphasis on the necessity of robust and adaptive security measures to safeguard sensitive healthcare information. Furthermore, this survey synthesizes current research on security frameworks and protocols tailored to HC4.0 applications, offering an in-depth analysis of their strengths, limitations, and applicability in real-world scenarios. We identify gaps in the literature and propose future research directions aimed at advancing encryption, authentication, and network resilience in interconnected healthcare systems. The concluding sections address ongoing challenges, open issues, and the need for scalable and interoperable security solutions to support the seamless integration of IoT technologies across healthcare and industrial sectors. By providing a holistic overview of the cybersecurity landscape in IoT, IIoT, and H-IoT, this paper contributes valuable insights to the development of secure, resilient, and sustainable systems in an increasingly connected world.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144473039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hybrid Meta-Heuristic Approach-Aided Optimal Cluster Head Selection and Multi-Objective Derivation for Energy Efficient Routing Protocol in Wireless Sensor Network","authors":"P. Kalyanasundaram,&nbsp;Rajesh Arunachalam,&nbsp;E. Mohan,&nbsp;P. Sherubha","doi":"10.1002/ett.70198","DOIUrl":"https://doi.org/10.1002/ett.70198","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless Sensor Networks (WSN) are utilized mostly for the collection of data, specifically to perform complex schemes. Thus, the issues of sensor networks and mission-critical sensors are the implementation of Energy Efficiency (EE) routing protocols. Thus, the EE routing protocol in the WSN model is developed in this work to improve the lifespan of the network for the WSN. The Fuzzy C-Means (FCM) clustering is performed for generating cluster groups and here the CHs are optimized using the Best and Worst Fitness of Sailfish Whale Optimization (BWF-SWO). To further evaluate the efficacy of the network, the fitness function is considered by Intra and Inter-cluster Distance and Residual Energy. To determine the efficiency of the routing process, diverse constraints like shortest path distance, throughput, energy consumption, hop count, latency, and Packet Delivery Ratio (PDR) are considered. In the end, the performance is calculated using divergent parameters and contrasted against existing methodologies. From the results, the energy consumption of the implemented EE protocol in WSN is minimized by 55% of RPO, 10% of COA, 20% of SFO, and 50% of WOA appropriately when the node count is 100. Thus, the findings explored that the proposed protocol achieved enriched outcomes on energy-efficient routing in the WSN model.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PEF-CAPD: A Privacy Enhanced Federated Cyber Physical and Attack Detection Framework for Edge-Cloud-Blockchain Enabled Smart Healthcare Environment","authors":"Muthu Pandeeswari Rajagopal,&nbsp;Gobalakrishnan Natesan","doi":"10.1002/ett.70187","DOIUrl":"https://doi.org/10.1002/ett.70187","url":null,"abstract":"<div>\u0000 \u0000 <p>Recently, healthcare industries faced severe cybersecurity problems due to the widespread amalgamation of technologies into a smart healthcare environment. As the number of attacks increased, the crucial healthcare sectors were targeted by cyber attackers. Conventional cybersecurity operations were not very effective due to their heterogeneity and complexity, respectively. In this research, we propose a novel privacy-preserving and attack detection framework named Privacy Enhanced Federated Cyber Physical and Attack Detection (PEF-CAPD) for the healthcare environment. The proposed research exploits edge computing, cloud computing, and federated learning technologies, respectively, to enhance the applicability and privacy in the healthcare environment. Initially, the medical data from the medical devices are securely encrypted and provided to the CMS. Note that the medical devices are connected in a MESH structure to enable self-healing, scalability, and reliability properties. In the CMS, the collected data are subjected to pre-processing, in which the pre-processed data are fed to the ES, where the patient-specific local models are generated using Skipped Dense Neural Network (SDNN) from local attack detection datasets. The generated local models are provided to the BCS for global model aggregation using the Novel Federated Aggregation Model (NFAM). From the aggregated global model, the Advanced Explainable Support Vector Machine (AEX-SVM) detects the possible attacks in the healthcare environment. The proposed work is validated on benchmark datasets that are generated from varied healthcare environments. The validation results show that the proposed approach demonstrates noteworthy accuracy of 99.92% compared to the state-of-the-art works.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Academic Performance and Career Mobility Through Hybrid Clustered Graph Neural Networks","authors":"Jisha Isaac,&nbsp;Vargheese Mary Amala Bai","doi":"10.1002/ett.70190","DOIUrl":"https://doi.org/10.1002/ett.70190","url":null,"abstract":"<div>\u0000 \u0000 <p>The main concern of the intelligence course recommendations is to improve college students' innovation and entrepreneurship learning experience. Thus, the need for individualized effective materials in modern education increases as much as the rates of online education platforms. Moreover, this expansion usually comes with various related drawbacks, and one of them is the problem of searching for classes that meet the learners' preferences and goals. When it comes to educational data, traditional methods of data processing fail to control such a huge amount of data and might even lead to distortions. To this end, this study presents the Hybrid Clustered Graph Neural Network to provide a more accurate analysis and prediction of students' academic performance for providing course recommendations. An efficient course recommendation framework named Hybrid Clustered Graph Neural Network is proposed for the career development of engineering students. The descriptor datasets were used for this research article which contains the details of course and user requirements. The collected descriptor data are preprocessed by imputation and normalization approaches to provide the enhanced quality and relevance of the data. In the feature extraction phase, the Clustering-based Graph Convolutional Representation model is implemented to extract student's recommendations and WordPieceFormer is applied for the extraction of contextual-based social media features. The Hybrid Clustered Recurrent Neural Network model is proposed for scoring and ranking the courses according to the recommendation ranking aspects. This study examines the behavioral performance using the proposed approach, providing appropriate course suggestions to achieve career mobility objectives. The evaluations indicated the viability of the proposed model, showing an accuracy efficiency of 98% and precision of 96.6%. The following results show the benefits of the proposed approach in attaining the appropriate recommendations that meet the students' academic performance and student career needs for providing course recommendations.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Actor-Critic Optimization Based Adaptive Task Scheduling Using Deep Double Dueling Q Network for Heterogeneous Cloud Environments","authors":"C. Felsy,&nbsp;R. Isaac Sajan","doi":"10.1002/ett.70185","DOIUrl":"https://doi.org/10.1002/ett.70185","url":null,"abstract":"<div>\u0000 \u0000 <p>In the rapidly evolving domain of cloud computing, the efficient scheduling of dependent tasks is critical for optimizing resource utilization and achieving key objectives such as minimizing latency and maximizing throughput. This paper presents the Actor-Critic Optimization based Adaptive Task Scheduling using Deep Double Dueling Q Network (ACTS-D3QN) in heterogeneous cloud environments enhances cloud task scheduling by incorporating advanced machine learning and optimization techniques. The D3QN framework is structured as an actor-critic model, where the actor component handles task scheduling and resource allocation, and the critic component refines these schedules. The actor component of D3QN integrates a Proportional Integral Derivative (PID) Controller for adaptive scheduling, ensuring real-time optimization of resource allocation while adhering to strict deadlines and dynamically managing workloads. Additionally, the system introduces a Dynamic Data Placement Algorithm with Predictive Caching (DDPPC), aimed at improving data locality and minimizing data transfer times. To balance operational costs with performance, a Modified NSGA-III algorithm is employed in the critic component of D3QN for multi-objective optimization. Furthermore, constraint programming is leveraged for efficient task-to-resource matching. Experimental results demonstrate that the ACTS-D3QN method achieves significant improvements, including a 22.14% reduction in makespan and a 20.0% increase in throughput, thereby validating its effectiveness in dynamic cloud environments.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-Efficient Model Decoupling for Personalized Federated Learning on Cloud-Edge Computing Networks","authors":"Chutong Jin,&nbsp;Tian Du,&nbsp;Xingyan Chen","doi":"10.1002/ett.70203","DOIUrl":"https://doi.org/10.1002/ett.70203","url":null,"abstract":"<div>\u0000 \u0000 <p>Federated Learning (FL) has emerged as a key distributed learning approach for privacy-preserving data scenarios. However, with the demonstrated effectiveness of scaling laws by large language models, the increasing parameter size of neural networks has led to substantial communication overhead, posing significant challenges for distributed learning systems. To address these issues, we propose a novel energy-efficient personalized federated learning framework called FedEMD, which utilizes model decoupling to divide deep neural networks into a body, consisting of the early layers of the network, and a personalized head, comprising the layers beyond the body. During training, the personalized head does not need to be uploaded to the central server for aggregation, thereby saving significant bandwidth resources. Additionally, we propose a performance-resource balancing mechanism that adaptively adjusts the number of body layers uploaded based on the available resource of the client. Finally, we conducted experiments on six datasets, comparing our method with five state-of-the-art model decoupling approaches. Our method was able to save about 10.7% in bandwidth consumption while providing comparable performance.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Subcarrier Allocation and Beamforming Optimization for IRS-Assisted Multiuser MISO-OFDMA Systems","authors":"Binh-Minh Vu,&nbsp;Oh-Soon Shin","doi":"10.1002/ett.70192","DOIUrl":"https://doi.org/10.1002/ett.70192","url":null,"abstract":"<p>In this article, we propose a novel resource allocation strategy for multiuser multiple-input single-output orthogonal frequency division multiple access (MU-MISO-OFDMA) systems within internet of things networks, utilizing an intelligent reflecting surface (IRS) to enhance system performance. Our goal is to maximize the sum rate for all networks by jointly optimizing transmit beamforming, IRS reflection coefficients, and OFDMA subcarrier allocation (SA). The problem is characterized as a mixed-integer nonlinear programming problem, which is inherently complex. To efficiently tackle the problem, we introduce an innovative framework that employs an alternative optimization of the beamforming matrix, IRS reflection coefficients, and the SA matrix. Additionally, we utilize the inner approximation method to address the nonconvex sub-problems related to beamforming and IRS reflection coefficients. Numerical results demonstrate the efficacy of the proposed approach while satisfying quality of service constraints. Notably, the proposed SA scheme substantially outperforms the system without SA, closely approaching the performance of the exhaustive search method while significantly reducing computational complexity.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Objective Collaborative Resource Allocation for Cloud-Edge Networks: A VNE Approach","authors":"Xin Li,&nbsp;Chengcheng Li,&nbsp;Weihong Dai,&nbsp;Konstantin Igorevich Kostromitin,&nbsp;Shengpeng Chen,&nbsp;Ning Chen","doi":"10.1002/ett.70197","DOIUrl":"https://doi.org/10.1002/ett.70197","url":null,"abstract":"<div>\u0000 \u0000 <p>The cloud-edge network (CEN) architecture has garnered significant attention due to its flexibility, reliability, and scalability in resource coordination and configuration. However, the generation of large-scale tasks has led to the urgent need for efficient resource allocation methods in CEN environments with limited computing resources. Virtual network embedding (VNE) technology enhances resource allocation flexibility by decoupling physical network resources and functions, allowing for adaptable integration of virtual networks (VNs) with underlying infrastructure. In this paper, we propose a deep reinforcement learning (DRL) based multi-domain VNE method, termed MD-VNE, for CEN resource allocation. Initially, the CEN is modeled as a multi-domain network with a series of associated resource constraints. Furthermore, we design an agent based on a multi-layer neural network to compute candidate CEN nodes and links. Finally, we validate the proposed method's advantages through extensive simulation experiments. The problem of efficient resource allocation in cloud-edge collaborative networks is effectively solved. Specifically, compared with the experimental baselines, the average improvements in the acceptance rate, long-term benefit and long-term benefit-to-cost ratio are <span></span><math></math>, <span></span><math></math>, and <span></span><math></math>, respectively.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secure and Traceable Decentralized Agri-Food Supply Chain Framework Using Ethereum Blockchain and IPFS Platform","authors":"Anandika Sharma,&nbsp;Tarunpreet Bhatia,&nbsp;Anupam Sharma,&nbsp;Pushan Aggarwal","doi":"10.1002/ett.70188","DOIUrl":"https://doi.org/10.1002/ett.70188","url":null,"abstract":"<div>\u0000 \u0000 <p>The multilayered structure of agri-food supply chain and involvement of several stakeholders complicates the management of critical factors such as product origin, production phases, pricing, and quality standards. These challenges often lead to inefficiencies, miscommunication, and vulnerabilities to fraud, undermining consumer trust and food safety. Blockchain technology offers a groundbreaking approach by providing secure, transparent, and efficient tracking mechanism across agri-food supply chain stakeholders. It supports real-time monitoring of food products, improves safety measures, upholds the quality standards, and builds trust among stakeholders. This study proposes a secure and transparent framework for the agri-food supply chain by applying the features of a private Ethereum 2.0 blockchain and smart contracts. The proposed framework provides traceability, minimizes fraudulent activities and improves overall supply chain integrity which ultimately benefiting both consumers and stakeholders. The proposed solution eliminates the dependency on intermediaries by providing stakeholders with complete visibility into transaction details, thereby enhancing food safety, quality, and reliability through a decentralized application. A key innovation of this framework is the consolidation of all functionalities into a comprehensive Ethereum smart contract, which significantly reduces contract complexity, gas consumption, and transaction fees, making the system more cost-effective and scalable for users. Furthermore, the integration of the Interplanetary File Storage System ensures efficient and reliable storage of information off-chain, reducing the burden on the blockchain platform while maintaining data integrity. The study individually evaluated the latency and throughput of various smart contract functions. The observed latency comes into the range from 8.3 s to 10.4 s, and the throughput, varying between 0.37 to 0.60 transactions per second which falls within the acceptable range of Ethereum testnet environment. Security analysis confirms the robustness and resilience of the framework, ensuring its suitability for real-world deployment.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Multi-Wavelet Oriented Auto-Encoder for Intrusion Detection in IoT System","authors":"Kuruba Madhusudhan,&nbsp;Aravind Kumar Madam","doi":"10.1002/ett.70202","DOIUrl":"https://doi.org/10.1002/ett.70202","url":null,"abstract":"<div>\u0000 \u0000 <p>IoT devices become more integrated into daily life, they are increasingly vulnerable to cyberattacks, compromising user confidentiality. Although existing intrusion detection techniques for IoT systems have been developed, they often fail to accurately classify attacks. This paper presents a novel approach for detecting intrusions in IoT devices by combining advanced feature extraction and deep learning techniques. The proposed method first pre-processes dataset images to enhance data quality by filtering out irrelevant information. A unique Aquila Optimized Convolutional Neural Network (AO-CNN) is then applied to extract optimal features. The proposed AO-CNN incorporates an optimization technique called Aquila Optimizer that fine-tunes the CNN's ability to extract more relevant and discriminative features from the IoT data. For attack detection, an innovative Attention-Based Multi-Wavelet-Oriented Autoencoder (AMV-AE) is designed for more precise attack classification. The Attention Mechanism is the model to focuses on the most relevant features, ensuring that the key patterns indicative of an attack are not lost during the detection process. Multi-Wavelet Transform enhances feature representation by capturing both time and frequency domain characteristics of the data, making it particularly effective in identifying subtle anomalies that may indicate an intrusion. The key novelty of this approach lies in the integration of AO-CNN for feature optimization and AMV-AE for superior detection accuracy. Evaluated on the NSL-KDD dataset, the model achieves a recall of 98.49% and an accuracy of 99.35% while demonstrating reduced inference time and memory usage, outperforming existing methods.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}